--- license: apache-2.0 configs: - config_name: extract-bench features: - name: id dtype: string - name: category dtype: string - name: pdf dtype: string - name: data_schema dtype: string - name: expected_output dtype: string - name: field_rules dtype: string - name: repeated_structure dtype: string - name: tags sequence: string data_files: - split: short path: short.jsonl - split: medium path: medium.jsonl - split: long path: long.jsonl language: - en pretty_name: ExtractBench size_categories: - n<1K tags: - document-extraction - structured-extraction - information-extraction - pdf - benchmark - evaluation - json-schema - visual-grounding - forms - tables citation: | @misc{extractbench2026, title={ExtractBench: A Benchmark for Schema-Guided Enterprise Document Extraction}, author={LlamaIndex}, year={2026}, url={https://github.com/run-llama/ExtractBench}, } --- # ExtractBench — smoke subset This is the `test-data` branch: a 6-document sample (3 short, 2 medium, 1 long) for quick smoke runs. **The benchmark itself lives on the [`main`](https://huggingface.co/datasets/llamaindex/ExtractBench) branch** — 370 documents across 4,869 pages — and that is what any reported score must be run against. The rows here are byte-identical to their counterparts on `main`, in the same format and with the same tags; only the number of documents differs. Use it to check that a pipeline is wired up correctly before spending a full run. ```bash uv run extract-bench download --test # -> data/test// uv run extract-bench run --test # inference -> evaluation -> reports ``` | Split | File | Documents | Example | |-------|------|----------:|---------| | Short | [short.jsonl](short.jsonl) | 3 | scanned tax return, handwritten-annotated disposal permit, pivoted election results | | Medium | [medium.jsonl](medium.jsonl) | 2 | district check register, quarterly earnings deck | | Long | [long.jsonl](long.jsonl) | 1 | 66-page clinical measurement listing | The sample spans eight challenge tags (long lists, cross-page continuation, pivoted tables, packed cells, needle-in-haystack, dense forms, filer-versus-reviewer edits), scanned and handwritten capture, and four business domains. Five of the six documents carry word-level bounding boxes in their ground truth, so every split reports grounding as well as value accuracy. The earnings deck has value-level ground truth only; grounding metrics are omitted for it, as they are for any document whose ground truth carries no boxes. - **Code**: [run-llama/ExtractBench](https://github.com/run-llama/ExtractBench) - **Full dataset**: [`main` branch](https://huggingface.co/datasets/llamaindex/ExtractBench)